Updated for filterpy 0.0.28

plot_gaussian() was deprecated because it was a poor name. Replaced
with plot_gaussian_pdf().

.gitignore

@@ -10,5 +10,6 @@ book.ipynb
 *.toc
 book.pdf
 book6x9.pdf
+Kalman_and_Bayesian_Filters_in_Python6x9.pdf
 Kalman_and_Bayesian_Filters_in_Python.pdf
 book_files

03-Gaussians.ipynb

@@ -778,9 +778,9 @@
     }
    ],
    "source": [
-    "from filterpy.stats import plot_gaussian\n",
-    "plot_gaussian(mean=1.8, variance=0.1414**2, \n",
-    "              xlabel='Student Height')"
+    "from filterpy.stats import plot_gaussian_pdf\n",
+    "plot_gaussian_pdf(mean=1.8, variance=0.1414**2, \n",
+    "                  xlabel='Student Height')"
    ]
   },
   {
@@ -794,7 +794,7 @@
     "\n",
     "This curve is not unique to heights - a vast amount of natural phenomena exhibits this sort of distribution, including the sensors that we use in filtering problems. As we will see, it also has all the attributes that we are looking for - it represents a unimodal belief or value as a probability, it is continuous, and it is computationally efficient. We will soon discover that it also other desirable qualities which we may not realize we desire.\n",
     "\n",
-    "To further motivate you, recall the shapes of the probability distributions in the *Discrete Bayes* chapter. They were not perfect Gaussian curves, but they were similar, as in the plot below. We will be using Gaussians to replace the discrete probabilities used in that chapter!"
+    "To further motivate you, recall the shapes of the probability distributions in the *Discrete Bayes* chapter. They were not perfect Gaussian curves, but they were similar, as in the plot below. We will be using Gaussians to replace the discrete probabilities used in that chapter! Please note that eyeball comparisons of PDF curves is strongly discouraged, as humans have trouble estimating areas; CDFs are usually the preferred choice. "
    ]
   },
   {
@@ -856,7 +856,7 @@
    ],
    "source": [
     "with figsize(y=3.):\n",
-    "    plot_gaussian(mean=120, variance=17**2, xlabel='speed(kph)')"
+    "    plot_gaussian_pdf(mean=120, variance=17**2, xlabel='speed(kph)')"
    ]
   },
   {
@@ -939,7 +939,7 @@
    ],
    "source": [
     "from filterpy.stats import gaussian, norm_cdf\n",
-    "plot_gaussian(22, 4, mean_line=True, xlabel='$^{\\circ}C$')"
+    "plot_gaussian_pdf(22, 4, mean_line=True, xlabel='$^{\\circ}C$')"
    ]
   },
   {

04-One-Dimensional-Kalman-Filters.ipynb

@@ -446,7 +446,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAAEWCAYAAABlmwYuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6dd6f98>"
+       "<matplotlib.figure.Figure at 0x72b99b0>"
       ]
      },
      "metadata": {},
@@ -486,9 +486,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6dcef60>"
+       "<matplotlib.figure.Figure at 0x72b92e8>"
       ]
      },
      "metadata": {},
@@ -531,9 +531,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6dd6588>"
+       "<matplotlib.figure.Figure at 0x72ec5f8>"
       ]
      },
      "metadata": {},
@@ -554,9 +554,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75909b0>"
+       "<matplotlib.figure.Figure at 0x7c23438>"
       ]
      },
      "metadata": {},
@@ -587,7 +587,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x770bc88>"
+       "<matplotlib.figure.Figure at 0x7d9f5c0>"
       ]
      },
      "metadata": {},
@@ -619,7 +619,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7885be0>"
+       "<matplotlib.figure.Figure at 0x7d9f908>"
       ]
      },
      "metadata": {},
@@ -653,9 +653,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAADaCAYAAADnoPfrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVOX+wPHPALIqKiKLioiKuBEquBHuiCllLolLVy9k\n/dJbhlpZlnVbvJY3I3PD6nrTa5pLmrmggloq7kggijuKCwwKIqgIAzPz+8Ma71wREJEzwPf9evXq\nxfc855zvmceB75x5zvOo9Hq9HiGEEEIIIUSlMFM6ASGEEEIIIWoSKcCFEEIIIYSoRFKACyGEEEII\nUYmkABdCCCGEEKISSQEuhBBCCCFEJZICXAghhBBCiEokBbgQQgghhBCVqEwF+KJFi/Dw8MDGxgY/\nPz9iY2Mf2ragoIDQ0FB8fHywtLSkT58+xbZbvnw5Pj4+2NnZ4erqytixY8nIyCjfVQghhBBCCFFF\nlFqAr169msmTJzNjxgwSEhLw9/dn4MCBXL58udj2Wq0WGxsbJk2aRHBwMCqV6oE2u3fvJjQ0lJde\neonk5GQ2bNjAyZMnefHFFx//ioQQQgghhDBhqtJWwuzatSsdOnTgm2++McRatWrFCy+8wKxZs0o8\n+Ouvv86JEyf49ddfjeJz5sxhwYIFXLx40RD7/vvveeONN7h161Y5LkMIIYQQQoiqocQ74BqNhvj4\neIKCgoziQUFB7N+/v9wn7d+/P9evX2fz5s3o9XoyMzNZtWoVwcHB5T6mEEIIIYQQVUGJBXhmZiZa\nrRZnZ2ejuJOTE2q1utwn9fHx4YcffmD06NFYWVnh5OQEwNKlS8t9TCGEEEIIIaoCCyVOevDgQUJD\nQ/noo48YMGAAaWlpvP3227z66qssW7bMqG1OTo4SKQohhBBCCFEh6tata/RziQW4o6Mj5ubmD8xO\nkpGRgaura7mT+OqrrwgMDOTNN98EoH379tjZ2dGjRw8+++wzGjVqVO5jCyGEEEIIYcpKHIJiaWmJ\nr68v0dHRRvGYmBj8/f3LfVK9Xo+ZmfGp//xZp9OV+7hCCCGEEEKYulKHoEydOpWxY8fSpUsX/P39\nWbx4MWq1mgkTJgAwffp0jhw5wo4dOwz7JCcno9FoyMzM5Pbt2yQmJqLX6+nQoQMAQ4YMITQ0lMWL\nFxMUFER6ejqTJ0/G19eXJk2aPDSX/719L56suLg4APz8/BTORID0hylRoi+u30znZOrvnEyN5+zl\nJDRFBWXe18rShnp2DbCxtsOqljXWtWywsrRBhQqdXoder0en11KgySev4DZ5+bfJy7/F7bu56Clx\noiwDM5UZbk4taOPeifbNO9PEqTlmqspZ603eG6ZF+sN0SF8oq6Rh1KUW4CEhIWRlZTFz5kzS09Px\n9vYmKioKNzc3ANRqNSkpKUb7BAcHk5qaCoBKpaJjx46oVCq0Wi0AY8aMIScnhwULFvDmm29Sr149\n+vbty+zZs8t9kUIIUZ3o9XrSs1L5/ew+fj+7n2vZV0tsb1XLGpcGTXF1cMOlQVNcHJrgYO9EXbsG\n2FjZliuHIm0h2bcyycrJICs3g4zsq1y9foGr1y+QV3DbqK1OryM14yypGWfZdng19nb1adfMjw6e\n/rRyewpzM/Ny5SCEENVRqfOAK+2/Pz3IHfDKJZ+cTYv0h+l4kn2RlZvBoeRd/H5mHxnZVx7arkFd\nZ1o0aouHa2uauXjh2sANs0oqcvV6Pdm3MknNOMP5qyc4d+UEaVmpD21fx6Yunbx64OvVE3dnz2IX\naHsc8t4wLdIfpkP6Qlkl1bCKzIIihBDivsKiQo5fOMyB4zGcvpRY7LAPSwsrPN28aePeiTbuHWlY\nr/wPwj8ulUqFg31DHOwb0tHzaQDu5N/izOVjHE85QvLFo9zJv7+o2q27OexO2MzuhM0412/C094D\n6NKmD7bWtZW6BCGEUJQU4EIIoZDcO9nsPRbFvqRobt99cKygZS1r2nt0pqPn07Rp1hFLCysFsiwb\nO+s6dPR8mo6eT6PTabmoPkPCuQPEn9lL7p1sQ7uM7Cus37OETfuX06lVD3o8NZCmzi0VzFwIISqf\nFOBCCFHJrl6/wK+/b+To6b1odUVG21SoaO3eka5t+9LeozOWtUy36H4YMzNzmjdqQ/NGbRgS8FfO\nXjlO3KndJJzbT0FhPgCFRRoOJe/kUPJOPJt40893KG3cO1b48BQhhDBFUoALIUQlSVWfYeuh1SRf\nPPrAtnq1G9CtXSDd2vbDwd5JgeyeDDMzc7ya+uDV1IfhvV/h6Ok9xB7bytXMi4Y2Z68kcfZKEo0a\nuNPPbxi+rQIqbTy7EEIoQQpwIYR4wi6qz7Dt4CqSU+Mf2Obh2preHQfzVIuu1X6mEGtLG572HoB/\n+yAuqs+wJ2Ezv5/dh05/b/2HtKxUlm//iujDaxnYbRQdPP0rbSpDIYSoTFKACyHEE3Llegqb9/3w\nQOGtQkUHT3/6dHqeZi6tFMpOOSqVCg9XLzxcvXj26b/w2++bOHA8xjC3eUb2FZZunUOjI80I7j6G\n9h6dZWiKEKJakQJcCCEq2M3bWWzZv4LDJ381mtFEhYpOXj0Y0GUELg5uCmZoOhrYOzO818s80yWE\n3Qlb+DVhIwWauwCkZV7ku02zcHdpxbCeL+Hh2lrhbIUQomJIAS6EEBUkX3OXnUfXsyv+FwqLNIa4\nFN6ls7OxZ1D30fTqEMzOoxvYk7jFcEc8VX2Gr9a8i69XTwY/PZb6dRoqnK0QQjweKcCFEOIx6fV6\n4k7v4Ze9S8nNyzba1q6ZH4MDxuHaoKlC2VUtdjb2DA4YR++Og9kRt469SVvRau/NFHP09B6OnT9I\nP9+h9PMdilUta4WzFUKI8inT0y2LFi3Cw8MDGxsb/Pz8iI2NfWjbgoICQkND8fHxwdLSkj59+hTb\nTqPR8OGHH9K8eXOsra1xd3dn/vz55bsKIYRQiPrGZeav/4Dl278yKr4bN/TgtaEf8+rzM6T4Lgd7\nu3oM6zWe98cuwKdld0O8sEjDtkOrmbV8EsdTjiiYoRBClF+pd8BXr17N5MmTiYyMJCAggIULFzJw\n4ECSk5Nxc3vwq1StVouNjQ2TJk1iy5YtRstw/rdRo0aRlpbGd999h6enJxkZGeTl5T3+FQkhRCUo\n1GrYGPsfdv3+Czqd1hCva+fAc0+Pxa91L5nBowI41nVhfPA7nL1ynPV7lnD1+gUAsm9d59tN/8Cn\nZXc863XG1spe4UyFEKLsSi3AIyIiCAsLY/z48QDMmzePbdu2ERkZyaxZsx5ob2trS2RkJAAJCQnc\nvHnzgTbR0dHs2rWLlJQUHBwcAGjaVO4QCSGqhrSbKRw4t4U7BfdvMJipzOjd8Tme6ToKa0sbBbOr\nnjybtOftUXM4lLyLjfuXc+duLgCJ5w6QbH6UDk370EnXUeYPF0JUCSXentFoNMTHxxMUFGQUDwoK\nYv/+/eU+6YYNG+jcuTNz5szBzc2NVq1aER4ezp07d8p9TCGEeNLuFuSxaudCdpxYaVR8N3dtw7Qx\nEQzpESbF9xNkZmZO9/b9mTF2Ad3a9jPEC7UajlzYzty173Et+6qCGQohRNmUeAc8MzMTrVaLs7Oz\nUdzJyQm1Wl3uk6akpBAbG4u1tTXr168nOzubSZMmkZaWxtq1ax+6X1xcXLnPKcpPXnfTIv2hjKvZ\n5zlwbgt5mlxDzNLCBr9m/Wjh5EPaxUzSLmYqmGHN0qp+d+zbN+LQ+Shy7mYBcFF9ms9+CKeTe19a\nu8rc4UqT31WmQ/pCGZ6eng/dpsgsKDqdDjMzM1auXEmdOnUAWLBgAQMGDOD69es0bChTTAkhTIOm\nKJ+4Czs4dy3BKN7UwYuuLQZiY1lbocyES113nu3wCsev7CfpSiw6vQ6trogjF6K5lHUKf8/nqGNd\nX+k0hRDiASUW4I6Ojpibm5ORkWEUz8jIwNXVtdwndXV1pVGjRobiG6B163sLLFy6dOmhBbifn1+5\nzyke3Z+fmOV1Nw3SH5UvJe0U/9n+LTdyrxlidtZ16NQ0kGaObencubOC2Yk/mZtZ4NbAi/grMaRl\nXgQgI/cSW44tYWiPMPzbB8nd8Eokv6tMh/SFsh42EQmUMgbc0tISX19foqOjjeIxMTH4+/uXO6GA\ngADS0tKMxnyfOXMGAHd393IfVwghKoJWp2XrwVV8/dN7RsV3h5b+vDd2Ph4N20lBZ2Ic7Jx5a9QX\nDOgywjD7jKYwn9W7IvnX5s8MD20KIYQpKHWOrKlTp7J06VKWLFnCyZMnCQ8PR61WM2HCBACmT59O\nYGCg0T7JyckkJCSQmZnJ7du3SUxMJCHh/te3Y8aMoUGDBoSFhZGcnMy+ffsIDw9nxIgRODo6VvAl\nCiFE2WXlZjD/pxlsPbQKvV4HgI2lLX995k1eCp5GHdt6CmcoHsbCvBbB3V9kSsjnONdvYognpRxm\n9sopnLt6QsHshBDivlLHgIeEhJCVlcXMmTNJT0/H29ubqKgowxzgarWalJQUo32Cg4NJTU0FQKVS\n0bFjR1QqFVrtvbly7ezs2LFjB5MmTaJz587Ur1+foUOH8vnnn1f09QkhRJkdPb2X1bsiydfcX5Og\nRaO2jB0wBQd7eTalqnB3acXbY75kY+x/2JO4BYCbt7OYv+4DBnQZwYAuIZjLdIVCCAWV6SHMiRMn\nMnHixGK3ff/99w/ELly4UOoxW7Vqxfbt28tyeiGEeKIKizSs3/Nv9iVtM8TMVGYM7DaK/n7DZW7p\nKsjSwooXer+CV1MfVsbM507+LfR6HdsOrebs5STGPTOF+nXkQ5UQQhmyTJsQokbLyslg7trpRsV3\nA3tnwkd8xoAuIVJ8V3Hezbvwzotzadm4nSF2Pi2Zf66cyqnUhBL2FEKIJ0cKcCFEjLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAADxCAYAAABPuZiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x789aa20>"
+       "<matplotlib.figure.Figure at 0x809add8>"
       ]
      },
      "metadata": {},
@@ -666,7 +666,7 @@
     "from book_format import set_figsize, figsize\n",
     "import filterpy.stats as stats\n",
     "with figsize(y=3):\n",
-    "    stats.plot_gaussian(mean=23, variance=5)"
+    "    stats.plot_gaussian_pdf(mean=23, variance=5)"
    ]
   },
   {
@@ -693,7 +693,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79edbe0>"
+       "<matplotlib.figure.Figure at 0x7de37b8>"
       ]
      },
      "metadata": {},
@@ -788,7 +788,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e1c048>"
+       "<matplotlib.figure.Figure at 0x85cca20>"
       ]
      },
      "metadata": {},
@@ -845,7 +845,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e70240>"
+       "<matplotlib.figure.Figure at 0x85d0d30>"
       ]
      },
      "metadata": {},
@@ -898,7 +898,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74d6240>"
+       "<matplotlib.figure.Figure at 0x86bb7f0>"
       ]
      },
      "metadata": {},
@@ -984,11 +984,20 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "None"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7627e10>"
+       "<matplotlib.figure.Figure at 0x8762048>"
       ]
      },
      "metadata": {},
@@ -1246,7 +1255,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x750b940>"
+       "<matplotlib.figure.Figure at 0x87be2e8>"
       ]
      },
      "metadata": {},
@@ -1307,7 +1316,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x769fda0>"
+       "<matplotlib.figure.Figure at 0x87c87f0>"
       ]
      },
      "metadata": {},
@@ -1447,7 +1456,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76419b0>"
+       "<matplotlib.figure.Figure at 0x98b9b38>"
       ]
      },
      "metadata": {},
@@ -1541,7 +1550,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x764a128>"
+       "<matplotlib.figure.Figure at 0x9820390>"
       ]
      },
      "metadata": {},
@@ -1712,7 +1721,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x742f908>"
+       "<matplotlib.figure.Figure at 0x7dd6f98>"
       ]
      },
      "metadata": {},
@@ -1742,7 +1751,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x73b1400>"
+       "<matplotlib.figure.Figure at 0x809a7b8>"
       ]
      },
      "metadata": {},
@@ -1873,7 +1882,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x73f65f8>"
+       "<matplotlib.figure.Figure at 0x86a2e10>"
       ]
      },
      "metadata": {},
@@ -1883,7 +1892,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x958fc88>"
+       "<matplotlib.figure.Figure at 0x98a1470>"
       ]
      },
      "metadata": {},
@@ -2023,7 +2032,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74e3048>"
+       "<matplotlib.figure.Figure at 0x8608c88>"
       ]
      },
      "metadata": {},
@@ -2089,7 +2098,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7641588>"
+       "<matplotlib.figure.Figure at 0x98b99b0>"
       ]
      },
      "metadata": {},
@@ -2147,7 +2156,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ab4ac8>"
+       "<matplotlib.figure.Figure at 0x9b09518>"
       ]
      },
      "metadata": {},
@@ -2210,7 +2219,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ac3208>"
+       "<matplotlib.figure.Figure at 0x9b094e0>"
       ]
      },
      "metadata": {},
@@ -2269,7 +2278,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x790c518>"
+       "<matplotlib.figure.Figure at 0x99c15c0>"
       ]
      },
      "metadata": {},
@@ -2315,7 +2324,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79a7da0>"
+       "<matplotlib.figure.Figure at 0x9a7d4e0>"
       ]
      },
      "metadata": {},
@@ -2370,7 +2379,17 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "None"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from IPython.html.widgets import interact, interactive, fixed\n",
     "from IPython.html.widgets import FloatSlider\n",
@@ -2410,11 +2429,20 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "None"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7aa3f28>"
+       "<matplotlib.figure.Figure at 0x99349e8>"
       ]
      },
      "metadata": {},
@@ -2508,9 +2536,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77a0eb8>"
+       "<matplotlib.figure.Figure at 0x9b1cd30>"
       ]
      },
      "metadata": {},
@@ -2597,9 +2625,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXWYFtUXxz+zXbAsXUt3C0gpICX5owRpQSQFREIBQelW\nkVSkle4GCZFG6e5mqaW38/7+uLs788L2vrk7n+fx8d55Jy47debcc75HEUIIdHR0dHR0dHR0dHTM\nhp2lB6Cjo6Ojo6Ojo6OT1tCNcB0dHR0dHR0dHR0zoxvhOjo6Ojo6Ojo6OmZGN8J1dHR0dHR0dHR0\nzIxuhOvo6Ojo6Ojo6OiYGd0I19HR0dHR0dHR0TEzuhGuo6Ojo6Ojo6OjY2aMaoSPGjUKOzs7g/9y\n5sxpzEPo6Ojo6Ojo6Ojo2DwOxt5hsWLF+Oeff2L69vb2xj6Ejo6Ojo6Ojo6Ojk1jdCPc3t6erFmz\nGnu3Ojo6Ojo6Ojo6OqkGo8eE37p1i1y5clGgQAHatWvH7du3jX0IHR0dHR0dHR0dHZtGEUIIY+1s\n586d+Pv7U6xYMZ48ecK4ceO4cuUKFy9eJGPGjMY6jI6Ojo6Ojo6Ojo5NY1Qj/G0CAwPJnz8/Q4cO\nZcCAAQC8fv3aVIfT0dHR0dHR0dHRMQuenp4p2t6kEoVubm6ULFmSGzdumPIwOjo6Ojo6Ojo6OjaF\nSY3w4OBgLl++TI4cOUx5GB0dHR0dHR0dHR2bwqjqKIMHD6Zp06Z4e3vz9OlTxo4dS1BQEJ07d451\n/ZS68XVSLydOnACgYsWKFh6JjjWjXyc6ieHt6+TQWUGNL+Vv9vZwaRk8eg6T/oSdx97d3sEePigD\nH1eC+pWhXGGws1OIjBScvApbDsn/zr416Zs5A3zeGHo2gwK5FFP+E3WMgP480UkMxgyrNqoR7uPj\nQ7t27Xj27BlZsmShatWqHDt2DG9vb2MexmicvibD4csVBkXRH5A6Ojo6aYHRC9V2x4+hsLdCYW+o\nUQ7OXBNMXgpr9kFkpFwnPAL2n5b/DZ8LWb2gcgnBiSvSeI+LZ69g6jL4cTnUryzo1RwaVwN7e/19\nY0v8d0mwcg/87wOoVUE/dzrGw6hG+IoVK4y5O5Oybp+g9QjZLlUAujcVdGoAGdLpN5iOjo5OauXg\nGcFe6fDE3h6GdzH8vVwRhRVjYOwDwYIt8Ne/cOa64TpPX8KWw+/u28kRaleQ75TVe+HeE7lcCOlh\n33kMvLPB3G8FDaro7xprJzxcMP4PGLcYIiJgxhrYM13wUXn93OkYB5PGhFsz87eo7Qu3oP8vkKsZ\nfD5OcPSCwISiMTo6Ojo6FkLrBe9UHwrljt2gKpRbYWJvhVOLFR5uhsUjoH09GWKiJZMndG4Ia8aB\n7zbY/pPClD4KN9fApsnQsApoJ1rvP4FPvpMedx3r5c4jQa1+MHqBNMBBzox0GAW+L/Vzp2McTCpR\nGBvaWBp7x/R4uJn/i9IvQJClMYSGxb1O6YIwuD10rK+HqlgCPTZPJzHo14lOYoi+TgIdKvBRH7nM\n3h6uLIeCcRjhcREZKTh9TcZ/F84N1UonHF5yy0fw+2ZYsAWeR70C8+WA4wsgk6f+frEWoq+T6y8r\n0HsqvAmIfb2GVWDLVJkXoJP20NqxVi1RmBAHz1rmuLuPqwZ4yfwwZ7CMC9dy/iZ0Hguz1pp/fDo6\nOraHEAL/QMHdx4KTVwS7/hWs2C2YuUYwbaXAx1f3nlma0QvUdqcGSTfAQRpeFYopdG2iUL2ckqj4\n7gK5FCb1Vjj4K6Rzk8vuPIJ2P8iQBx3rwD/YjpFL89FhlGqA29vDmO6weYq63o5j8JPtRN/qWDFG\njQlPKntOQMOq5j/u1iNqu1kN6NVCoWdzmWQzdyOs3AOBwfL3gTOheD5B3ff1L14dHZ3Y+XG5YMxC\n8A+Ke53Z6+DMEmGR2T8dOHXDg32nZNveHkbELtplUorlVfjjB0GLobK/5wQM/x0mf2n+segYcva6\noNOUEvg8d45Zlj8nLBsJVUrJe3Zwe8GPy+Vvw+dC9bIi5jed1MPZ64IyhcwTBWFRT3h0cow5iYwU\nbNcY4f/7QP5fURTeL64wf5iCzyaoVEIuj4iANt/DjQe6t0JHR+dddv8n+HZ2/AY4wK2H0uDSsQzz\nduSMaX/W0HKSgc2qK4zoovanLoPVe/X3iyU5d0NQ5ysMDPBODeD0YgyM7PE9oUpJ2Q6PgHYj4eUb\n/dylJu48ElToCqU6wtyNps8PtKgRfu4GPHlh3gv4+GWZ2Q5SZur94u+u4+mhsH4C5Mgk+y/9oPkQ\neBMQ/1gjIgQXbglCQvWbUkcnLfDslaDLOLXv7AS5s0LZQlIlo3VtaFVL/X3WWjh8Tn8+mJuT1z04\neSMdIDW/LeEF1zLqC2ikmQX+YiJcuKVfF5bg8h1Bvf7w4o3su7tEsGwULPleIb274Yeao4PC8tGQ\nQV5K3H0M3SYSq6EWGSk4flmwbp8gMFg/t7bC7HUyAffyHVi3z/TecIuro/x90rzH08pKNaoWd2JF\nziwKGybJlyrApTvQcbQ0tGPj4Bn59VSmE1TqBqFh+k2no2OL+AeKOO9zLUIIuk1UdaKzesGddXBv\ng8LpJQp7ZiisGquwaiw0qBK9jXxpB4fozwdzIYTgd40XvHMjyJ/TsiEEdnYKS0dCodyyHxAELYbq\nXtXEEhEZwY5/V7Hp0GKCQxOYgoqH6/cFdb8C31ey7+4Swew+12hXL+7rI18OhQXD1P6GA9JwA/ns\n2LBf8MVEQa5mULkbtB4BDQfGbTvoWA8BQYIFW9X+V5+a/pgWN8L3mDkkZavGCI8ORYmLSiUU5g01\n3Pb7eYbr+PgKOo4S1OwjPfsgkzo3HTTOeHV0dMzH0r8Enh9D0bZw6mr8L825G2HzIbW/8DvIlvHd\nl7eiKPz2DXi4yv7VezBmkTFHrRMf/5yC0zdVL/hwC3vBo8mQTmHDRHCPui5u+kCnMdKDqhM/527+\ny45jK9h7ciN7T65P1j5uP5QhKNEf0R6uML3XdUrkCUxw2xY1Ffp8ovYHz4KP+wsyN5Lyk4u2wpMX\n6u8Hz8K8zckapo4Z+WMnvPKT7UK5DWerTIXljfDjsU/lmIJ7j0WMoezkCPXeT3ibjvUVvumg9if9\nCSt2C0LDBFOWCYq1g+W7391uvn7D6ejYFK/8BP2nSW/1rYdQ40vYeCD2Z9PlO4JBM9V+31bQqFrc\n3rM82RUmaZLvpi5P2MjXMQ7jFqvtLo2lJ9NaKFlAYdFwtb/9KBRuA31+Emw9LAgI0q+R2AgM9otp\nv3jjm+Tt7z+RBviDp7Lv6gxbf4Qy+ePQJIyFqX3gvSKyHRomHYpvyx67OKnt7+bCU11f3GqJjBTM\nXKP2+7YyjwSlxY3w+0/gxgPzHEurilKrPIlWKZjQ8634vQlQuhMMnSOnEaNpVl0tyrD7uPzS1tHR\nsQ1+XCHzP6IJDJZerclLDZNzQkIF7UdCUIjslyoAUxKhbtGruSyLDjLhu9tECNPl6UzK4XNCVUSx\nEwzrZNnxxEarWgpDOqr92w/h1/XQ9FvI3Ajqfy0lLnVxABVXZ/eYdmh4SJK2fegrDfA7j2Tf2UkW\nVapRLmkGl4uzwsox6gxXNGUKwbDP4PBc8N0OBXPJ5a/84NtZSTqEjhnZfRyu3JXtdG7QpZF5jmtx\nIxzMF5KiDUVpkkAoihZ7e4Vlo6BYXtkPDoXr99XfS+aHvTNgwySF+pXV5drYIh0dHevl8XPBL6vU\nvldU4pUQMOxX+eEdnefx3VxZqAXkC3z5aPlCTgg7OxneFu0dO3Mdpiwz5r9C520mLFHbDSs+t3gs\neFyM6wGD2qka4tGEhErjYNBMGSL1xw7dEAdwc/aIaQcF+yd6uycvBHX7q44/RwdYP4FkSxAX9lbY\nN0t6TWcPljkhZ5YojO+pULWUgrurwsyB6vp/7IT9p/VzaI3MWK22uzTmnaRcU2EVRvje46Y/hn+g\nMEgCbVQ1Er/A1zx+cZ8Xb54muL2nh8KmyWpWtFwG0/rDqcVQq4I8Yd3+p/6+eJteiEFHxxYYv0St\nDVC2EFxZoXqtARZvh3r9ZSjatJXq8ilfQqkCiX9YF/ZWGN1N7Y9dBJdu688IU3DyimDHMdlWFEHn\nuo8tO6B4sLdXmNpXwXe7dOgMbi9nWLQIAUPmoCttAG4uqhEeGJK4EJKbDwQf9lK9nQ72sHocNKya\nMmOrQjGFGQMUerdQyJP93X01qKIYKCT1+VEXbrA2rt3TPiugXyvzHdsqjPC/T5k+c1gbr1W6INz3\n/Yvh8zoz4c9+7D6+LlH7KOytsPNnqF8Z+rWGqyuh/6cKjg7qjfe/DyFbRtl++EzG+Ono6Fgvt3wE\nv29S++N7QhYvhV2/SI9INAfPQodRar9RVekBSyoD2kDFYrIdGibDUnTlBOMz8Q+1XbfcS/JmS1rY\ngiVwclSoVUFhSh+Fc38q3F0Pc4dA9ii53CcvVCWOtIw2HCUwJGFP+Kmrgg96yeRXADs7WDZKarab\ng5+/UsNWLt2BX1bHu7qOmZmpqYzeuBoUSkYl3eRiUSM8Wof7lR+cumbaY215KxTFwzV9TN8/6HWi\n91OphMKOnxWmf62Q1evdE+XooNBZE0s0f0uyhqujo2MmRi2AsHDZrl5WreLr5CilyCZ9qeZ6RJPV\nCxYOT56GrIODwoLv5FQ4wLGLhi8BnZRz8ZZg/X6136We9XrB48M7m0L3pgo/fK4um7IM/BKoWZHa\nESKS4nnLU63Ux1QtWTfedfccF3zUR60P4uIE6yZA69rmM7RyZ1UYpZkBG7MQ7j5O2+fQWnjtL1iy\nXe1/1dq8x7eoEV5Xo05iyuqZb1fJfNcIf2PU42lDUrYfhQdP9ZvNmvHxFbT9XtBjsLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74dbfd0>"
+       "<matplotlib.figure.Figure at 0x8628cc0>"
       ]
      },
      "metadata": {},

12-Particle-Filters.ipynb

@@ -28,6 +28,7 @@
        "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
        "@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
        "@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
+       "@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
        "\n",
        "    div.cell{\n",
        "        width: 900px;\n",
@@ -118,9 +119,9 @@
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
-       "        font-family: 'Arimo',verdana,arial,sans-serif;\n",
+       "        font-family: 'Fira sans', verdana,arial,sans-serif;\n",
        "        line-height: 125%;\n",
-       "        font-size: 120%;\n",
+       "        font-size: 115%;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
@@ -322,9 +323,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x791cba8>"
+       "<matplotlib.figure.Figure at 0x49a6e10>"
       ]
      },
      "metadata": {},
@@ -398,7 +399,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84fcda0>"
+       "<matplotlib.figure.Figure at 0x84e1ba8>"
       ]
      },
      "metadata": {},
@@ -476,7 +477,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c9e5f8>"
+       "<matplotlib.figure.Figure at 0x896d550>"
       ]
      },
      "metadata": {},
@@ -529,9 +530,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAEWCAYAAAAn/SKQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8500da0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x894be10>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvcAAAEtCAYAAACMMk9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x84e4f60>"
       ]
      },
      "metadata": {},
@@ -539,8 +550,8 @@
     }
    ],
    "source": [
-    "from filterpy.stats import plot_gaussian\n",
-    "plot_gaussian(mean=2, variance=3)"
+    "from filterpy.stats import plot_gaussian_pdf\n",
+    "plot_gaussian_pdf(mean=2, variance=3)"
    ]
   },
   {
@@ -565,7 +576,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x937e780>"
+       "<matplotlib.figure.Figure at 0x8905080>"
       ]
      },
      "metadata": {},
@@ -808,7 +819,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9361c50>"
+       "<matplotlib.figure.Figure at 0x84a4668>"
       ]
      },
      "metadata": {},
@@ -920,7 +931,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x938c198>"
+       "<matplotlib.figure.Figure at 0x84cb630>"
       ]
      },
      "metadata": {},
@@ -975,7 +986,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f07b38>"
+       "<matplotlib.figure.Figure at 0x8ea6b70>"
       ]
      },
      "metadata": {},
@@ -1031,7 +1042,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9574a90>"
+       "<matplotlib.figure.Figure at 0x9510940>"
       ]
      },
      "metadata": {},
@@ -1071,7 +1082,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xaf8ee80>"
+       "<matplotlib.figure.Figure at 0xbf706a0>"
       ]
      },
      "metadata": {},
@@ -1199,7 +1210,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAABACAYAAAC+2bgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOP8CSdsd9j7kQ7EAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x93c19b0>"
+       "<matplotlib.figure.Figure at 0xbf52518>"
       ]
      },
      "metadata": {},
@@ -1242,7 +1253,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb381b38>"
+       "<matplotlib.figure.Figure at 0x9f7dac8>"
       ]
      },
      "metadata": {},
@@ -1316,7 +1327,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95855c0>"
+       "<matplotlib.figure.Figure at 0x89176a0>"
       ]
      },
      "metadata": {},
@@ -1361,7 +1372,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c1a358>"
+       "<matplotlib.figure.Figure at 0x7deab70>"
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      },
      "metadata": {},
@@ -1424,7 +1435,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x85686a0>"
+       "<matplotlib.figure.Figure at 0x84e60f0>"
       ]
      },
      "metadata": {},
@@ -1490,7 +1501,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x853c3c8>"
+       "<matplotlib.figure.Figure at 0x8e2cd30>"
       ]
      },
      "metadata": {},
@@ -1500,7 +1511,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb39c978>"
+       "<matplotlib.figure.Figure at 0x8e812b0>"
       ]
      },
      "metadata": {},
@@ -1510,7 +1521,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x935bc50>"
+       "<matplotlib.figure.Figure at 0x8ec03c8>"
       ]
      },
      "metadata": {},
@@ -1520,7 +1531,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f37780>"
+       "<matplotlib.figure.Figure at 0x9f725f8>"
       ]
      },
      "metadata": {},
@@ -1593,7 +1604,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,